{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.使用 lightGBM 预测音乐推荐结果     模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import pickle as pk\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgbm\n",
    "from lightgbm.sklearn import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../Data/'  # 文件路径\n",
    "model_path = '../model/' # 模型路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 载入标签数据\n",
    "with open(model_path + 'target_list.pkl','rb') as fr:\n",
    "    train_Y = pk.load(fr)\n",
    "fr.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(model_path + 'data_all_train_lgbm_v1.pkl','wb') as fw:\n",
    "#     pk.dump(train_X,fw)\n",
    "# fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(model_path + 'data_all_train_lgbm_v1.pkl','rb') as fr:\n",
    "    train_X = pk.load(fr)\n",
    "fr.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = train_X.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['source_system_tab', 'source_screen_name', 'source_type', 'city', 'bd',\n",
       "       'gender', 'registered_via', 'song_length', 'language',\n",
       "       'reg_interval_days',\n",
       "       ...\n",
       "       'genre_id_157', 'genre_id_158', 'genre_id_159', 'genre_id_160',\n",
       "       'genre_id_161', 'genre_id_162', 'genre_id_163', 'genre_id_164',\n",
       "       'genre_id_165', 'genre_id_166'],\n",
       "      dtype='object', length=181)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM超参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM的主要的超参包括：\n",
    "1. 树的数目n_estimators 和 学习率 learning_rate\n",
    "2. 树的最大深度max_depth 和 树的最大叶子节点数目num_leaves（注意：XGBoost只有max_depth，LightGBM采用叶子优先的方式生成树，num_leaves很重要，设置成比 2^max_depth 小）\n",
    "3. 叶子结点的最小样本数:min_data_in_leaf(min_data, min_child_samples)\n",
    "4. 每棵树的列采样比例：feature_fraction/colsample_bytree\n",
    "5. 每棵树的行采样比例：bagging_fraction （需同时设置bagging_freq=1）/subsample\n",
    "6. 正则化参数lambda_l1(reg_alpha), lambda_l2(reg_lambda)\n",
    "7. 两个非模型复杂度参数，但会影响模型速度和精度。可根据特征取值范围和样本数目修改这两个参数\n",
    "1）特征的最大bin数目max_bin：默认255；\n",
    "2）用来建立直方图的样本数目subsample_for_bin：默认200000。\n",
    "\n",
    "对n_estimators，用LightGBM内嵌的cv函数调优，因为同XGBoost一样，LightGBM学习的过程内嵌了cv，速度极快。\n",
    "其他参数用GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_ROUNDS = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相同的交叉验证分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 5.48 µs\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# #直接调用 lightgbm 内嵌的交叉验证(cv)，可对连续的 n_estimators 参数进行快速交叉验证\n",
    "# #而GridSearchCV只能对有限个参数进行交叉验证，且速度相对较慢\n",
    "# def get_n_estimators(params , train_X , train_Y , early_stopping_rounds=10):\n",
    "#     lgbm_params = params.copy()\n",
    "     \n",
    "#     lgbmtrain = lgbm.Dataset(train_X , train_Y)\n",
    "     \n",
    "#     #num_boost_round为弱分类器数目，下面的代码参数里因为已经设置了early_stopping_rounds\n",
    "#     #即性能未提升的次数超过过早停止设置的数值，则停止训练\n",
    "#     cv_result = lgbm.cv(lgbm_params , lgbmtrain , num_boost_round=MAX_ROUNDS , nfold=3,  metrics='auc' , early_stopping_rounds=early_stopping_rounds,seed=3 )\n",
    "     \n",
    "#     return cv_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 为了得到 n_estimators ，需要为其他重要的参数赋一个初始值，如 params 中的值。\n",
    "初始值的意义不大，只是为了方便确定其他参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1d 22min 7s, sys: 27min 11s, total: 1d 49min 18s\n",
      "Wall time: 4h 8min 53s\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# params = {'boosting_type': 'gbdt',\n",
    "#           'objective': 'regression',\n",
    "#           'n_jobs': 6,\n",
    "#           'learning_rate': 0.1,\n",
    "#           'num_leaves': 60,\n",
    "#           'max_depth': 6,\n",
    "#           'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "#           'subsample': 0.7,\n",
    "#           'bagging_freq': 1,\n",
    "#           'colsample_bytree': 0.7,\n",
    "#          }\n",
    "\n",
    "# cv_result = get_n_estimators(params , train_X , train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cv_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('best_score:%f '% cv_result['auc-mean'][-10:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_n_estimators = len(cv_result['auc-mean'])\n",
    "# print(\"best estimator's num: %d\" % n_estimators)\n",
    "best_n_estimators = 9431"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. num_leaves & max_depth=7\n",
    "num_leaves建议70-80，搜索区间50-80,值越大模型越复杂，越容易过拟合\n",
    "相应的扩大max_depth=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_leaves_s = range(50,90,10) #50,60,70,80\n",
    "# tuned_parameters = dict(num_leaves = num_leaves_s)\n",
    "# print(tuned_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9h 9min 47s, sys: 15min 24s, total: 9h 25min 12s\n",
      "Wall time: 2h 12min 46s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "               colsample_bytree=0.5, importance_type='split',\n",
       "               learning_rate=0.01, max_bin=64, max_depth=7,\n",
       "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "               n_estimators=9431, n_jobs=6, num_leaves=30,\n",
       "               objective='regression', random_state=None, reg_alpha=0.0,\n",
       "               reg_lambda=0.0, silent=False, subsample=0.7,\n",
       "               subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'regression',\n",
    "          'n_jobs': 6,\n",
    "          'learning_rate': 0.01,\n",
    "          'min_child_samples':20,\n",
    "          'n_estimators':best_n_estimators,\n",
    "          'max_depth': 7,\n",
    "          'max_bin': 64, #原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.5,\n",
    "          'num_leaves': 30,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "# num_leaves_s = range(10,50,20) #50,60,70,80\n",
    "# tuned_parameters = dict( num_leaves = num_leaves_s)\n",
    "lg.fit(train_X,train_Y)\n",
    "# print('tuned_parameters',tuned_parameters)\n",
    "# grid_search_num_leaves = GridSearchCV(lg, n_jobs=6, param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=10, refit = False)\n",
    "# grid_search_num_leaves.fit(train_X , train_Y)\n",
    "#grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    with open(model_path + 'lg_v2_2_1.pkl','wb') as fw:\n",
    "        pk.dump(lg,fw)\n",
    "    fw.close()\n",
    "except Exception as e:\n",
    "    print('dump lg_v2_2_1.pkl error,',e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9h 27min 35s, sys: 20min 39s, total: 9h 48min 15s\n",
      "Wall time: 2h 43min 53s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "               colsample_bytree=0.8, importance_type='split',\n",
       "               learning_rate=0.01, max_bin=64, max_depth=7,\n",
       "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "               n_estimators=9431, n_jobs=6, num_leaves=30,\n",
       "               objective='regression', random_state=None, reg_alpha=0.0,\n",
       "               reg_lambda=0.0, silent=False, subsample=0.7,\n",
       "               subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#  提高 'colsample_bytree': 0.8,\n",
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'regression',\n",
    "          'n_jobs': 6,\n",
    "          'learning_rate': 0.01,\n",
    "          'min_child_samples':20,\n",
    "          'n_estimators':best_n_estimators,\n",
    "          'max_depth': 7,\n",
    "          'max_bin': 64, #原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.8,\n",
    "          'num_leaves': 30,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "# num_leaves_s = range(10,50,20) #50,60,70,80\n",
    "# tuned_parameters = dict( num_leaves = num_leaves_s)\n",
    "lg.fit(train_X,train_Y)\n",
    "# print('tuned_parameters',tuned_parameters)\n",
    "# grid_search_num_leaves = GridSearchCV(lg, n_jobs=6, param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=10, refit = False)\n",
    "# grid_search_num_leaves.fit(train_X , train_Y)\n",
    "#grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    with open(model_path + 'lg_v2_2_2.pkl','wb') as fw:\n",
    "        pk.dump(lg,fw)\n",
    "    fw.close()\n",
    "except Exception as e:\n",
    "    print('dump lg_v2_2_2.pkl error,',e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看各个维度的重要性\n",
    "df = pd.DataFrame({\"columns\":list(column_names), \"importance\":list(lg.feature_importances_.T)})\n",
    "df = df.sort_values(by=['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>artist_name</td>\n",
       "      <td>31935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>name</td>\n",
       "      <td>25843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>composer</td>\n",
       "      <td>22867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>reg_interval_days</td>\n",
       "      <td>21109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city</td>\n",
       "      <td>20573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lyricist</td>\n",
       "      <td>19064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>source_type</td>\n",
       "      <td>18081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>source_screen_name</td>\n",
       "      <td>16406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>source_system_tab</td>\n",
       "      <td>13860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bd</td>\n",
       "      <td>13701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>language</td>\n",
       "      <td>10498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>registered_via</td>\n",
       "      <td>10179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gender</td>\n",
       "      <td>7113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>genre_id_14</td>\n",
       "      <td>3872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>genre_id_104</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>song_length</td>\n",
       "      <td>2353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>genre_id_78</td>\n",
       "      <td>2353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>genre_id_156</td>\n",
       "      <td>2138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>genre_id_2</td>\n",
       "      <td>1571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>genre_id_19</td>\n",
       "      <td>1476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>genre_id_15</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>genre_id_87</td>\n",
       "      <td>1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>genre_id_35</td>\n",
       "      <td>1245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>genre_id_111</td>\n",
       "      <td>1118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>genre_id_36</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>genre_id_88</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>genre_id_65</td>\n",
       "      <td>963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>genre_id_33</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>genre_id_124</td>\n",
       "      <td>637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>genre_id_127</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>genre_id_49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>genre_id_137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>genre_id_68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>genre_id_89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>genre_id_103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>genre_id_85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>genre_id_105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>genre_id_84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>genre_id_82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>genre_id_79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>genre_id_77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>genre_id_72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>genre_id_116</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>genre_id_69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>genre_id_118</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>genre_id_120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>genre_id_135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>genre_id_121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>genre_id_67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>genre_id_64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>genre_id_125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>genre_id_59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>genre_id_54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>genre_id_53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>genre_id_52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>genre_id_130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>genre_id_96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>genre_id_132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>genre_id_48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>genre_id_166</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                columns  importance\n",
       "10          artist_name       31935\n",
       "13                 name       25843\n",
       "11             composer       22867\n",
       "9     reg_interval_days       21109\n",
       "3                  city       20573\n",
       "12             lyricist       19064\n",
       "2           source_type       18081\n",
       "1    source_screen_name       16406\n",
       "0     source_system_tab       13860\n",
       "4                    bd       13701\n",
       "8              language       10498\n",
       "6        registered_via       10179\n",
       "5                gender        7113\n",
       "28          genre_id_14        3872\n",
       "118        genre_id_104        3400\n",
       "7           song_length        2353\n",
       "92          genre_id_78        2353\n",
       "170        genre_id_156        2138\n",
       "16           genre_id_2        1571\n",
       "33          genre_id_19        1476\n",
       "29          genre_id_15        1413\n",
       "101         genre_id_87        1272\n",
       "49          genre_id_35        1245\n",
       "125        genre_id_111        1118\n",
       "50          genre_id_36        1087\n",
       "102         genre_id_88         990\n",
       "79          genre_id_65         963\n",
       "47          genre_id_33         801\n",
       "138        genre_id_124         637\n",
       "141        genre_id_127         613\n",
       "..                  ...         ...\n",
       "63          genre_id_49           0\n",
       "151        genre_id_137           0\n",
       "82          genre_id_68           0\n",
       "103         genre_id_89           0\n",
       "117        genre_id_103           0\n",
       "99          genre_id_85           0\n",
       "119        genre_id_105           0\n",
       "98          genre_id_84           0\n",
       "96          genre_id_82           0\n",
       "93          genre_id_79           0\n",
       "91          genre_id_77           0\n",
       "86          genre_id_72           0\n",
       "130        genre_id_116           0\n",
       "83          genre_id_69           0\n",
       "132        genre_id_118           0\n",
       "134        genre_id_120           0\n",
       "149        genre_id_135           0\n",
       "135        genre_id_121           0\n",
       "81          genre_id_67           0\n",
       "78          genre_id_64           0\n",
       "139        genre_id_125           0\n",
       "73          genre_id_59           0\n",
       "68          genre_id_54           0\n",
       "67          genre_id_53           0\n",
       "66          genre_id_52           0\n",
       "144        genre_id_130           0\n",
       "110         genre_id_96           0\n",
       "146        genre_id_132           0\n",
       "62          genre_id_48           0\n",
       "180        genre_id_166           0\n",
       "\n",
       "[181 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(lg.feature_importances_)), lg.feature_importances_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 后面是genre_ids 特征离散化得到的，整体上后面离散化后的特征重要性偏低。其他的经过labelEncode编码后的特征重要性高很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
